AIMC Topic: Longitudinal Studies
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Curating a knowledge base for patients with neurosyphilis: a study protocol of a DEep learning Framework for pErsonalized prediction of Adverse prognosTic events in NeuroSyphilis (DEFEAT-NS).
BMJ open
Jul 18, 2025
INTRODUCTION: Adverse prognostic events (APE) of neurosyphilis include ongoing syphilitic meningitis, meningovascular syphilis, parenchymatous neurosyphilis and death. Its complexity and rarity have the potential to result in the underestimated true ...
Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.
BMC medical research methodology
Jul 17, 2025
BACKGROUND: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both co...
Artificial intelligence-based diabetes risk prediction from longitudinal DXA bone measurements.
Scientific reports
Jul 16, 2025
Diabetes mellitus (DM) is a serious global health concern that poses a significant threat to human life. Beyond its direct impact, diabetes substantially increases the risk of developing severe complications such as hypertension, cardiovascular disea...
Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study.
BMC medical research methodology
Jul 14, 2025
BACKGROUND: Missing survey data can threaten the validity and generalizability of findings from longitudinal cohort studies. Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data ...
PREACT-digital: study protocol for a longitudinal, observational multicentre study on digital phenotypes of non-response to cognitive behavioural therapy for internalising disorders.
BMJ open
Jul 10, 2025
INTRODUCTION: Cognitive behavioural therapy (CBT) serves as a first-line treatment for internalising disorders (ID), encompassing depressive, anxiety or obsessive-compulsive disorders. Nonetheless, a substantial proportion of patients do not experien...
Longitudinal studies on breastfeeding among preterm infants: a scoping review.
BMC pregnancy and childbirth
Jul 9, 2025
AIM: This study aims to assess the exclusive breastfeeding rate among preterm infants, examine the factors influencing breastfeeding practices, and identify evidence-based interventions to enhance lactation support.
Machine learning models for predicting multimorbidity trajectories in middle-aged and elderly adults.
Scientific reports
Jul 9, 2025
Multimorbidity has emerged as a significant public health issue in the context of global population aging. Predicting and managing the progression of multimorbidity in the elderly population is crucial. This study aims to develop predictive models fo...
Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.
BMJ health & care informatics
Jul 7, 2025
OBJECTIVES: To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.
Impact of generative AI interaction and output quality on university students' learning outcomes: a technology-mediated and motivation-driven approach.
Scientific reports
Jul 5, 2025
This study investigates the influence of generative artificial intelligence (GAI) on university students' learning outcomes, employing a technology-mediated learning perspective. We developed and empirically tested an integrated model, grounded in in...
Predicting Quality of Life in People Living with HIV: A Machine Learning Model Integrating Multidimensional Determinants.
Health and quality of life outcomes
Jul 4, 2025
OBJECTIVE: With survival steadily improving among people living with HIV(PLWH), quality of life (QoL) has emerged as the ultimate benchmark of therapeutic success. We therefore aimed to develop and validate machine learning models that predict QoL tr...
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